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   "cell_type": "markdown",
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   "source": [
    "# Welcome to your first assignment!\n",
    "\n",
    "Instructions are below. Please give this a try, and look in the solutions folder if you get stuck (or feel free to ask me!)"
   ]
  },
  {
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   "id": "ada885d9-4d42-4d9b-97f0-74fbbbfe93a9",
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   "source": [
    "<table style=\"margin: 0; text-align: left;\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#f71;\">Just before we get to the assignment --</h2>\n",
    "            <span style=\"color:#f71;\">I thought I'd take a second to point you at this page of useful resources for the course. This includes links to all the slides.<br/>\n",
    "            <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">https://edwarddonner.com/2024/11/13/llm-engineering-resources/</a><br/>\n",
    "            Please keep this bookmarked, and I'll continue to add more useful links there over time.\n",
    "            </span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e9fa1fc-eac5-4d1d-9be4-541b3f2b3458",
   "metadata": {},
   "source": [
    "# HOMEWORK EXERCISE ASSIGNMENT\n",
    "\n",
    "Upgrade the day 1 project to summarize a webpage to use an Open Source model running locally via Ollama rather than OpenAI\n",
    "\n",
    "You'll be able to use this technique for all subsequent projects if you'd prefer not to use paid APIs.\n",
    "\n",
    "**Benefits:**\n",
    "1. No API charges - open-source\n",
    "2. Data doesn't leave your box\n",
    "\n",
    "**Disadvantages:**\n",
    "1. Significantly less power than Frontier Model\n",
    "\n",
    "## Recap on installation of Ollama\n",
    "\n",
    "Simply visit [ollama.com](https://ollama.com) and install!\n",
    "\n",
    "Once complete, the ollama server should already be running locally.  \n",
    "If you visit:  \n",
    "[http://localhost:11434/](http://localhost:11434/)\n",
    "\n",
    "You should see the message `Ollama is running`.  \n",
    "\n",
    "If not, bring up a new Terminal (Mac) or Powershell (Windows) and enter `ollama serve`  \n",
    "And in another Terminal (Mac) or Powershell (Windows), enter `ollama pull llama3.2`  \n",
    "Then try [http://localhost:11434/](http://localhost:11434/) again.\n",
    "\n",
    "If Ollama is slow on your machine, try using `llama3.2:1b` as an alternative. Run `ollama pull llama3.2:1b` from a Terminal or Powershell, and change the code below from `MODEL = \"llama3.2\"` to `MODEL = \"llama3.2:1b\"`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e2a9393-7767-488e-a8bf-27c12dca35bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "from IPython.display import Markdown, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29ddd15d-a3c5-4f4e-a678-873f56162724",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constants\n",
    "\n",
    "OLLAMA_API = \"http://localhost:11434/api/chat\"\n",
    "HEADERS = {\"Content-Type\": \"application/json\"}\n",
    "MODEL = \"llama3.2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dac0a679-599c-441f-9bf2-ddc73d35b940",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a messages list using the same format that we used for OpenAI\n",
    "\n",
    "messages = [\n",
    "    {\"role\": \"user\", \"content\": \"Describe some of the business applications of Generative AI\"}\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bb9c624-14f0-4945-a719-8ddb64f66f47",
   "metadata": {},
   "outputs": [],
   "source": [
    "payload = {\n",
    "        \"model\": MODEL,\n",
    "        \"messages\": messages,\n",
    "        \"stream\": False\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "479ff514-e8bd-4985-a572-2ea28bb4fa40",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's just make sure the model is loaded\n",
    "\n",
    "!ollama pull llama3.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42b9f644-522d-4e05-a691-56e7658c0ea9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If this doesn't work for any reason, try the 2 versions in the following cells\n",
    "# And double check the instructions in the 'Recap on installation of Ollama' at the top of this lab\n",
    "# And if none of that works - contact me!\n",
    "\n",
    "response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n",
    "print(response.json()['message']['content'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a021f13-d6a1-4b96-8e18-4eae49d876fe",
   "metadata": {},
   "source": [
    "# Introducing the ollama package\n",
    "\n",
    "And now we'll do the same thing, but using the elegant ollama python package instead of a direct HTTP call.\n",
    "\n",
    "Under the hood, it's making the same call as above to the ollama server running at localhost:11434"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7745b9c4-57dc-4867-9180-61fa5db55eb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ollama\n",
    "\n",
    "response = ollama.chat(model=MODEL, messages=messages)\n",
    "print(response['message']['content'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4704e10-f5fb-4c15-a935-f046c06fb13d",
   "metadata": {},
   "source": [
    "## Alternative approach - using OpenAI python library to connect to Ollama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23057e00-b6fc-4678-93a9-6b31cb704bff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# There's actually an alternative approach that some people might prefer\n",
    "# You can use the OpenAI client python library to call Ollama:\n",
    "\n",
    "from openai import OpenAI\n",
    "ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n",
    "\n",
    "response = ollama_via_openai.chat.completions.create(\n",
    "    model=MODEL,\n",
    "    messages=messages\n",
    ")\n",
    "\n",
    "print(response.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1622d9bb-5c68-4d4e-9ca4-b492c751f898",
   "metadata": {},
   "source": [
    "# NOW the exercise for you\n",
    "\n",
    "Take the code from day1 and incorporate it here, to build a website summarizer that uses Llama 3.2 running locally instead of OpenAI; use either of the above approaches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef76cfc2-c519-4cb2-947a-64948517913d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "from IPython.display import Markdown, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a151a8de-1e90-4190-b68e-b44b25a2cdd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constants\n",
    "\n",
    "OLLAMA_API = \"http://localhost:11434/api/chat\"\n",
    "HEADERS = {\"Content-Type\": \"application/json\"}\n",
    "MODEL = \"llama3.2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "700fffc1-c7b0-4001-b381-5c4fd28c8799",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reusing the Website BeautifulSoup wrapper from Day 1\n",
    "# SSL Verification has been disabled\n",
    "\n",
    "headers = {\n",
    " \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
    "}\n",
    "\n",
    "class Website:\n",
    "\n",
    "    def __init__(self, url):\n",
    "        \"\"\"\n",
    "        Create this Website object from the given url using the BeautifulSoup library\n",
    "        \"\"\"\n",
    "        self.url = url\n",
    "        response = requests.get(url, headers=headers, verify=False)  # NOTE Disabled ssl verification here to workaround VPN Limitations\n",
    "        soup = BeautifulSoup(response.content, 'html.parser')\n",
    "        self.title = soup.title.string if soup.title else \"No title found\"\n",
    "        for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
    "            irrelevant.decompose()\n",
    "        self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "402d5686-4e76-4110-b65a-b3906c35c0a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def user_prompt_for(website):\n",
    "    user_prompt = f\"You are looking at a website titled {website.title}\"\n",
    "    user_prompt += \"\\nThe contents of this website are as follows; \\\n",
    "please provide a short summary of this website in markdown. \\\n",
    "If it includes news or announcements, then summarize these too.\\n\\n\"\n",
    "    user_prompt += website.text\n",
    "    return user_prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81f5f140-8f77-418f-a252-8ad5d11f6c5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "## enter the web URL here:\n",
    "website_url = \"https://www.timecube.net/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d0ce4aa-b43e-4642-bcbd-d5964700ece8",
   "metadata": {},
   "outputs": [],
   "source": [
    "## This will at first print a warning for SSL which can be ignored before providing response. \n",
    "\n",
    "import ollama\n",
    "\n",
    "system_prompt = \"You are a virtual assistant who analyzes the contents of a website \\\n",
    "and provides a short summary, ignoring text that might be navigation related. \\\n",
    "Respond in markdown.\"\n",
    "\n",
    "messages = [\n",
    "    {\"role\": \"system\", \"content\": system_prompt},\n",
    "    {\"role\": \"user\", \"content\": user_prompt_for(Website(website_url))}\n",
    "]\n",
    "\n",
    "response = ollama.chat(model=MODEL, messages=messages)\n",
    "print(response['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "910b7e06-c92d-47bf-a4ee-a006d70deb06",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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